Implementation of pattern recognition techniques as applied to geoscience interpretation
Statistical system using statistical decision theory for data interpretation
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Statistical system using statistical decision theory for data interpretation
Applying statistical decision theory to manual adaptive control systems
Visual flash duration discrimination and decision theory analysis of effects of temporal and brightness differences
Using statistical decision theory analysis to obtain predicted performance in discrimination of brief empty time intervals between auditory signals
Linear dynamic systems state estimation, using empirical Bayes decision theory to develop filter set
The design criteria and test options for aerospace structural reliability were investigated. A decision methodology was developed for selecting a combination of structural tests and structural design factors. The decision method involves the use of Bayesian statistics and statistical decision theory. Procedures are discussed for obtaining and updating data-based probabilistic strength distributions for aerospace structures when test information is available and for obtaining subjective distributions when data are not available. The techniques used in developing the distributions are explained.
Mathematical and philosophical approaches are presented for evaluation and implementation of ground and air transportation systems. Basic decision processes are examined that are used for cost analyses and planning (i.e, statistical decision theory, linear and dynamic programming, optimization, game theory). The effects on the environment and the community that a transportation system may have are discussed and modelled. Algorithmic structures are examined and selected bibliographic annotations are included. Transportation dynamic models were developed. Citizen participation in transportation projects (i.e, in Maryland and Massachusetts) is discussed. The relevance of the modelling and evaluation approaches to air transportation (i.e, airport planning) is examined in a case study in St. Louis, Missouri.
The overall objective is to identify those areas of future missions which will be impacted by planetary quarantine (PQ) constraints. The objective of the phase being described was to develop an approach for using decision theory in performing a PQ analysis for a Mariner Jupiter Uranus Mission and to compare it with the traditional approach used for other missions.
Tables and charts are presented for experiment schematics, mission opportunities, mission timelines, crew time line and power profile. The methodology being developed to evaluate cost effective vibroacoustic test plans for a representative EVAL shuttle spacelab payload is analyzed. Statistical decision theory is used to quantitatively evaluate seven alternate test plans which include component, subassembly, or payload testing and combinations of component assembly testing.
For each of the methods identified, a theoretical basis is provided and an illustrative example is described. The example includes sufficient realism and detail to enable an analyst to conduct an evaluation of other systems. The methods discussed in the study include equal capability cost analysis, consumers' surplus, and statistical decision theory.
Statistical decision theory is used to evaluate seven alternate vibro-acoustic test plans for Space Shuttle payloads; test plans include component, subassembly and payload testing and combinations of component and assembly testing. The optimum test levels and the expected cost are determined for each test plan. By including all of the direct cost associated with each test plan and the probabilistic costs due to ground test and flight failures, the test plans which minimize project cost are determined. The lowest cost approach eliminates component testing and maintains flight vibration reliability by performing subassembly tests at a relatively high acoustic level.
This paper is directed toward the development of the methodology needed to evaluate cost effective vibroacoustic test plans for Shuttle Spacelab payloads. Statistical decision theory is used to quantitatively evaluate seven alternate test plans by deriving optimum test levels and the expected cost for each multiple mission payload considered. The results indicate that minimum costs can vary by as much as $6 million for the various test plans. The lowest cost approach eliminates component testing and maintains flight vibration reliability by performing subassembly tests at a relatively high acoustic level. Test plans using system testing or combinations of component and assembly level testing are attractive alternatives. Component testing alone is shown not to be cost effective.
Methods have been developed for the evaluation of cost effective vibroacoustic test plans for Shuttle Spacelab payloads. The shock and vibration environments of components have been statistically represented, and statistical decision theory has been used to evaluate the cost effectiveness of five basic test plans with structural test options for two of the plans. Component, subassembly, and payload testing have been performed for each plan along with calculations of optimum test levels and expected costs. The tests have been ranked according to both minimizing expected project costs and vibroacoustic reliability. It was found that optimum costs may vary up to $6 million with the lowest plan eliminating component testing and maintaining flight vibration reliability via subassembly tests at high acoustic levels.
The reported investigation had the objective to evaluate the utility of satellite multispectral remote sensing in urban water resources planning. The results are presented of a study which was conducted to determine the economic impact of Landsat data. The use of Landsat data to estimate hydrologic model parameters employed in urban water resources planning is discussed. A decision regarding an employment of the Landsat data has to consider the tradeoff between data accuracy and cost. Bayesian decision theory is used in this connection. It is concluded that computer-aided interpretation of Landsat data is a highly cost-effective method of estimating the percentage of impervious area.
Compound decision theory is employed to develop a general statistical model for classifying image data using spatial context. The classification algorithm developed from this model exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. A key input to this contextural classifier is a quantitative characterization of this tendency: the context function. Several methods for estimating the context function are explored, and two complementary methods are recommended. The contextural classifier is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by a non-contextural uniform-priors maximum likelihood classifier when these methods of estimating the context function are used. An approximate algorithm, which cuts computational requirements by over one-half, is presented. The search for an optimal implementation is furthered by an exploration of the relative merits of using spectral classes or information classes for classification and/or context function estimation.
Information is given in the form of outlines, graphs, tables and charts. Topics include system identification, Bayesian statistical decision theory, Maximum Likelihood Estimation, identification methods, structural mode identification using a stochastic realization algorithm, and identification results regarding membrane simulations and X-29 flutter flight test data.
Synthetic aperture radar utilizes coherent microwaves to produce images of the earth's surface. Due to the interference of coherent wavelets, the images appear speckled. This reduces the performance of per-pel classifiers. One way to increase the performance is to filter the image first, then classify the filtered image. For this purpose, several novel filters that have been reported in the literature are investigated. These are the geometric filter, adaptive LMMSE filter, and linear approximation filter. For comparison, conventional mean and median filters are also considered. It is found that the mean filter with seven iterations gives the best result. The overall performance increased from 65.2 to 88.9 percent. The capability of these filters to preserve edges in the original image are also assessed. It is seen that the geometric and median filters are the best in preserving edges, and that the linear approximation and adaptive LMMSE filters are the best in discriminating roads. Prefiltering the image effectively provides contextual information to the per-pel classifier. An alternate approach is to directly design a contextual classifier. A new contextual classifier based on sequential decision theory is proposed. With this classifier, it is found that the overall performance increases to 89.5 percent.
Information on automation for deep space vehicle monitoring is given in viewgraph form. Information is given on automation goals and strategy; the Monitor Analyzer of Real-time Voyager Engineering Link (MARVEL); intelligent input data management; decision theory for making tradeoffs; dynamic tradeoff evaluation; evaluation of anomaly detection results; evaluation of data management methods; system level analysis with cooperating expert systems; the distributed architecture of multiple expert systems; and event driven response.